Chapter 6: Congestion Control and Resource Allocation

Report
Computer Networks: A Systems Approach, 5e
Larry L. Peterson and Bruce S. Davie
Chapter 6
Congestion Control and
Resource Allocation
Copyright © 2010, Elsevier Inc. All rights Reserved
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Chapter 6
Problem
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We have seen enough layers of protocol
hierarchy to understand how data can be
transferred among processes across
heterogeneous networks
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Problem
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How to effectively and fairly allocate resources among
a collection of competing users?
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Chapter 6
Chapter Outline
Issues in Resource Allocation
Queuing Disciplines
TCP Congestion Control
Congestion Avoidance Mechanism
Quality of Service
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Resources
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Chapter 6
Congestion Control and Resource Allocation
Bandwidth of the links
Buffers at the routers and switches
Packets contend at a router for the use of a link,
with each contending packet placed in a queue
waiting for its turn to be transmitted over the link
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When too many packets are contending for the
same link
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The queue overflows
Packets get dropped
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Chapter 6
Congestion Control and Resource Allocation
Network is congested!
Network should provide a congestion control
mechanism to deal with such a situation
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Congestion control and Resource Allocation
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Chapter 6
Congestion Control and Resource Allocation
Two sides of the same coin
If the network takes active role in allocating
resources
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The congestion may be avoided
No need for congestion control
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Congestion Control and Resource Allocation
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Allocating resources with any precision is difficult
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Resources are distributed throughout the network
On the other hand, we can always let the
sources send as much data as they want
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Then recover from the congestion when it occurs
Easier approach but it can be disruptive because
many packets many be discarded by the network
before congestions can be controlled
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Congestion Control and Resource Allocation
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Congestion control and resource allocations
involve both hosts and network elements such
as routers
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In network elements
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Various queuing disciplines can be used to control the
order in which packets get transmitted and which
packets get dropped
At the hosts’ end
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The congestion control mechanism paces how fast
sources are allowed to send packets
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Issues in Resource Allocation
Network Model
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Packet Switched Network
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We consider resource allocation in a packet-switched network
(or internet) consisting of multiple links and switches (or
routers).
In such an environment, a given source may have more than
enough capacity on the immediate outgoing link to send a
packet, but somewhere in the middle of a network, its packets
encounter a link that is being used by many different traffic
sources
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Chapter 6
Issues in Resource Allocation
Network Model
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Packet Switched Network
A potential bottleneck router.
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Chapter 6
Issues in Resource Allocation
Network Model
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Connectionless Flows
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For much of our discussion, we assume that the network is
essentially connectionless, with any connection-oriented
service implemented in the transport protocol that is running
on the end hosts.
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We need to qualify the term “connectionless” because our classification
of networks as being either connectionless or connection-oriented is a
bit too restrictive; there is a gray area in between.
In particular, the assumption that all datagrams are completely
independent in a connectionless network is too strong.
The datagrams are certainly switched independently, but it is usually the
case that a stream of datagrams between a particular pair of hosts
flows through a particular set of routers
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Chapter 6
Issues in Resource Allocation
Network Model
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Connectionless Flows
Multiple flows passing through a set of
routers
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Issues in Resource Allocation
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Network Model
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Connectionless Flows
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One of the powers of the flow abstraction is that flows can be
defined at different granularities. For example, a flow can be
host-to-host (i.e., have the same source/destination host
addresses) or process-to-process (i.e., have the same
source/destination host/port pairs).
In the latter case, a flow is essentially the same as a channel,
as we have been using that term throughout this book. The
reason we introduce a new term is that a flow is visible to the
routers inside the network, whereas a channel is an end-toend abstraction.
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Issues in Resource Allocation
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Network Model
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Connectionless Flows
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Because multiple related packets flow through each router, it
sometimes makes sense to maintain some state information
for each flow, information that can be used to make resource
allocation decisions about the packets that belong to the flow.
This state is sometimes called soft state.
The main difference between soft state and “hard” state is
that soft state need not always be explicitly created and
removed by signalling.
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Issues in Resource Allocation
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Network Model
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Connectionless Flows
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Soft state represents a middle ground between a purely
connectionless network that maintains no state at the routers
and a purely connection-oriented network that maintains hard
state at the routers.
In general, the correct operation of the network does not
depend on soft state being present (each packet is still routed
correctly without regard to this state), but when a packet
happens to belong to a flow for which the router is currently
maintaining soft state, then the router is better able to handle
the packet.
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Issues in Resource Allocation
Taxonomy
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Router-centric versus Host-centric
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In a router-centric design, each router takes responsibility for
deciding when packets are forwarded and selecting which
packets are to dropped, as well as for informing the hosts that
are generating the network traffic how many packets they are
allowed to send.
In a host-centric design, the end hosts observe the network
conditions (e.g., how many packets they are successfully
getting through the network) and adjust their behavior
accordingly.
Note that these two groups are not mutually exclusive.
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Taxonomy
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Reservation-based versus Feedback-based
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In a reservation-based system, some entity (e.g., the end
host) asks the network for a certain amount of capacity to be
allocated for a flow.
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Each router then allocates enough resources (buffers and/or
percentage of the link’s bandwidth) to satisfy this request. If the request
cannot be satisfied at some router, because doing so would overcommit
its resources, then the router rejects the reservation.
In a feedback-based approach, the end hosts begin sending
data without first reserving any capacity and then adjust their
sending rate according to the feedback they receive.
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This feedback can either be explicit (i.e., a congested router sends a
“please slow down” message to the host) or it can be implicit (i.e., the
end host adjusts its sending rate according to the externally observable
behavior of the network, such as packet losses).
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Issues in Resource Allocation
Taxonomy
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Window-based versus Rate-based
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Window advertisement is used within the network to reserve
buffer space.
Control sender’s behavior using a rate, how many bit per
second the receiver or network is able to absorb.
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Issues in Resource Allocation
Evaluation Criteria
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Effective Resource Allocation
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A good starting point for evaluating the effectiveness of a
resource allocation scheme is to consider the two principal
metrics of networking: throughput and delay.
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Clearly, we want as much throughput and as little delay as possible.
Unfortunately, these goals are often somewhat at odds with each other.
One sure way for a resource allocation algorithm to increase throughput
is to allow as many packets into the network as possible, so as to drive
the utilization of all the links up to 100%.
We would do this to avoid the possibility of a link becoming idle because
an idle link necessarily hurts throughput.
The problem with this strategy is that increasing the number of packets
in the network also increases the length of the queues at each router.
Longer queues, in turn, mean packets are delayed longer in the network
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Issues in Resource Allocation
Evaluation Criteria
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Effective Resource Allocation
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To describe this relationship, some network designers have
proposed using the ratio of throughput to delay as a metric for
evaluating the effectiveness of a resource allocation scheme.
This ratio is sometimes referred to as the power of the
network.
Power = Throughput/Delay
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Issues in Resource Allocation
Evaluation Criteria
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Effective Resource Allocation
Ratio of throughput to delay as a
function of load
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Evaluation Criteria
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Fair Resource Allocation
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The effective utilization of network resources is not the only
criterion for judging a resource allocation scheme.
We must also consider the issue of fairness. However, we
quickly get into murky waters when we try to define what
exactly constitutes fair resource allocation.
For example, a reservation-based resource allocation
scheme provides an explicit way to create controlled
unfairness.
With such a scheme, we might use reservations to enable a
video stream to receive 1 Mbps across some link while a file
transfer receives only 10 Kbps over the same link.
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Issues in Resource Allocation
Evaluation Criteria
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Fair Resource Allocation
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In the absence of explicit information to the contrary, when
several flows share a particular link, we would like for each
flow to receive an equal share of the bandwidth.
This definition presumes that a fair share of bandwidth means
an equal share of bandwidth.
But even in the absence of reservations, equal shares may
not equate to fair shares.
Should we also consider the length of the paths being
compared?
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Consider the figure in next slide.
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Evaluation Criteria
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Fair Resource Allocation
One four-hop flow competing with three one-hop flows
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Evaluation Criteria
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Fair Resource Allocation
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Assuming that fair implies equal and that all paths are of
equal length, networking researcher Raj Jain proposed a
metric that can be used to quantify the fairness of a
congestion-control mechanism.
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Jain’s fairness index is defined as follows. Given a set of flow
throughputs (x1, x2, . . . , xn) (measured in consistent units such as
bits/second), the following function assigns a fairness index to the flows:
The fairness index always results in a number between 0 and 1, with 1
representing greatest fairness.
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Queuing Disciplines
The idea of FIFO queuing, also called first-come-firstserved (FCFS) queuing, is simple:
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The first packet that arrives at a router is the first packet to be
transmitted
Given that the amount of buffer space at each router is finite, if a
packet arrives and the queue (buffer space) is full, then the router
discards that packet
This is done without regard to which flow the packet belongs to
or how important the packet is. This is sometimes called tail drop,
since packets that arrive at the tail end of the FIFO are dropped
Note that tail drop and FIFO are two separable ideas. FIFO is a
scheduling discipline—it determines the order in which packets
are transmitted. Tail drop is a drop policy—it determines which
packets get dropped
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(a) FIFO queuing; (b) tail drop at a FIFO queue.
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A simple variation on basic FIFO queuing is priority
queuing. The idea is to mark each packet with a priority;
the mark could be carried, for example, in the IP header.
The routers then implement multiple FIFO queues, one
for each priority class. The router always transmits
packets out of the highest-priority queue if that queue is
nonempty before moving on to the next priority queue.
Within each priority, packets are still managed in a FIFO
manner.
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Fair Queuing
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The main problem with FIFO queuing is that it does
not discriminate between different traffic sources, or it
does not separate packets according to the flow to
which they belong.
Fair queuing (FQ) is an algorithm that has been
proposed to address this problem. The idea of FQ is
to maintain a separate queue for each flow currently
being handled by the router. The router then services
these queues in a sort of round-robin,
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Fair Queuing
Round-robin service of four flows at a router
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Queuing Disciplines
Fair Queuing
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The main complication with Fair Queuing is that the
packets being processed at a router are not
necessarily the same length.
To truly allocate the bandwidth of the outgoing link in
a fair manner, it is necessary to take packet length
into consideration.
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For example, if a router is managing two flows, one with
1000-byte packets and the other with 500-byte packets
(perhaps because of fragmentation upstream from this
router), then a simple round-robin servicing of packets from
each flow’s queue will give the first flow two thirds of the link’s
bandwidth and the second flow only one-third of its
bandwidth.
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Fair Queuing
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What we really want is bit-by-bit round-robin; that is,
the router transmits a bit from flow 1, then a bit from
flow 2, and so on.
Clearly, it is not feasible to interleave the bits from
different packets.
The FQ mechanism therefore simulates this behavior
by first determining when a given packet would finish
being transmitted if it were being sent using bit-by-bit
round-robin, and then using this finishing time to
sequence the packets for transmission.
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Fair Queuing
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To understand the algorithm for approximating bit-bybit round robin, consider the behavior of a single flow
For this flow, let
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Pi : denote the length of packet i
Si: time when the router starts to transmit packet i
Fi: time when router finishes transmitting packet i
Clearly, Fi = Si + Pi
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Fair Queuing
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When do we start transmitting packet i?
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Depends on whether packet i arrived before or after the
router finishes transmitting packet i-1 for the flow
Let Ai denote the time that packet i arrives at the
router
Then Si = max(Fi-1, Ai)
Fi = max(Fi-1, Ai) + Pi
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Fair Queuing
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Now for every flow, we calculate Fi for each packet
that arrives using our formula
We then treat all the Fi as timestamps
Next packet to transmit is always the packet that has
the lowest timestamp
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The packet that should finish transmission before all others
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Fair Queuing
Example of fair queuing in action: (a) packets with earlier finishing times
are sent first; (b) sending of a packet already in progress is completed
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TCP Congestion Control
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TCP congestion control was introduced into the
Internet in the late 1980s by Van Jacobson,
roughly eight years after the TCP/IP protocol
stack had become operational.
Immediately preceding this time, the Internet was
suffering from congestion collapse—
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hosts would send their packets into the Internet as
fast as the advertised window would allow, congestion
would occur at some router (causing packets to be
dropped), and the hosts would time out and retransmit
their packets, resulting in even more congestion
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The idea of TCP congestion control is for each
source to determine how much capacity is
available in the network, so that it knows how
many packets it can safely have in transit.
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Once a given source has this many packets in transit,
it uses the arrival of an ACK as a signal that one of its
packets has left the network, and that it is therefore
safe to insert a new packet into the network without
adding to the level of congestion.
By using ACKs to pace the transmission of packets,
TCP is said to be self-clocking.
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TCP Congestion Control
Additive Increase Multiplicative Decrease
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TCP maintains a new state variable for each
connection, called CongestionWindow, which is used
by the source to limit how much data it is allowed to
have in transit at a given time.
The congestion window is congestion control’s
counterpart to flow control’s advertised window.
TCP is modified such that the maximum number of
bytes of unacknowledged data allowed is now the
minimum of the congestion window and the advertised
window
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Additive Increase Multiplicative Decrease
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TCP’s effective window is revised as follows:
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MaxWindow = MIN(CongestionWindow, AdvertisedWindow)
EffectiveWindow = MaxWindow − (LastByteSent −
LastByteAcked).
That is, MaxWindow replaces AdvertisedWindow in
the calculation of EffectiveWindow.
Thus, a TCP source is allowed to send no faster than
the slowest component—the network or the
destination host—can accommodate.
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Additive Increase Multiplicative Decrease
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The problem, of course, is how TCP comes to learn an
appropriate value for CongestionWindow.
Unlike the AdvertisedWindow, which is sent by the receiving side
of the connection, there is no one to send a suitable
CongestionWindow to the sending side of TCP.
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The answer is that the TCP source sets the CongestionWindow
based on the level of congestion it perceives to exist in the network.
This involves decreasing the congestion window when the level of
congestion goes up and increasing the congestion window when the
level of congestion goes down. Taken together, the mechanism is
commonly called additive increase/multiplicative decrease (AIMD)
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Additive Increase Multiplicative Decrease
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The key question, then, is how does the source
determine that the network is congested and that it
should decrease the congestion window?
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The answer is based on the observation that the main reason
packets are not delivered, and a timeout results, is that a
packet was dropped due to congestion. It is rare that a packet
is dropped because of an error during transmission.
Therefore, TCP interprets timeouts as a sign of congestion
and reduces the rate at which it is transmitting.
Specifically, each time a timeout occurs, the source sets
CongestionWindow to half of its previous value. This halving
of the CongestionWindow for each timeout corresponds to
the “multiplicative decrease” part of AIMD.
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Additive Increase Multiplicative Decrease
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Although CongestionWindow is defined in terms of
bytes, it is easiest to understand multiplicative
decrease if we think in terms of whole packets.
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For example, suppose the CongestionWindow is currently set
to 16 packets. If a loss is detected, CongestionWindow is set
to 8.
Additional losses cause CongestionWindow to be reduced to
4, then 2, and finally to 1 packet.
CongestionWindow is not allowed to fall below the size of a
single packet, or in TCP terminology, the maximum segment
size (MSS).
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Additive Increase Multiplicative Decrease
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A congestion-control strategy that only decreases the
window size is obviously too conservative.
We also need to be able to increase the congestion
window to take advantage of newly available capacity
in the network.
This is the “additive increase” part of AIMD, and it
works as follows.
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Every time the source successfully sends a
CongestionWindow’s worth of packets—that is, each packet
sent out during the last RTT has been ACKed—it adds the
equivalent of 1 packet to CongestionWindow.
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Additive Increase Multiplicative Decrease
Packets in transit during additive increase, with one packet being
added each RTT.
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Additive Increase Multiplicative Decrease
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Note that in practice, TCP does not wait for an entire window’s
worth of ACKs to add 1 packet’s worth to the congestion window,
but instead increments CongestionWindow by a little for each
ACK that arrives.
Specifically, the congestion window is incremented as follows
each time an ACK arrives:
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Increment = MSS × (MSS/CongestionWindow)
CongestionWindow+= Increment
That is, rather than incrementing CongestionWindow by an entire
MSS bytes each RTT, we increment it by a fraction of MSS every
time an ACK is received.
Assuming that each ACK acknowledges the receipt of MSS bytes,
then that fraction is MSS/CongestionWindow.
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Slow Start
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The additive increase mechanism just described is the
right approach to use when the source is operating
close to the available capacity of the network, but it
takes too long to ramp up a connection when it is
starting from scratch.
TCP therefore provides a second mechanism,
ironically called slow start, that is used to increase the
congestion window rapidly from a cold start.
Slow start effectively increases the congestion
windowexponentially, rather than linearly.
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Slow Start
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Specifically, the source starts out by setting
CongestionWindow to one packet.
When the ACK for this packet arrives, TCP adds 1 to
CongestionWindow and then sends two packets.
Upon receiving the corresponding two ACKs, TCP
increments CongestionWindow by 2—one for each
ACK—and next sends four packets.
The end result is that TCP effectively doubles the
number of packets it has in transit every RTT.
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Slow Start
Packets in transit during slow start.
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Slow Start
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There are actually two different situations in which
slow start runs.
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The first is at the very beginning of a connection, at which
time the source has no idea how many packets it is going to
be able to have in transit at a given time.
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In this situation, slow start continues to double CongestionWindow each
RTT until there is a loss, at which time a timeout causes multiplicative
decrease to divide CongestionWindow by 2.
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Slow Start
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There are actually two different situations in which
slow start runs.
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The second situation in which slow start is used is a bit more
subtle; it occurs when the connection goes dead while waiting
for a timeout to occur.
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Recall how TCP’s sliding window algorithm works—when a packet is
lost, the source eventually reaches a point where it has sent as much
data as the advertised window allows, and so it blocks while waiting for
an ACK that will not arrive.
Eventually, a timeout happens, but by this time there are no packets in
transit, meaning that the source will receive no ACKs to “clock” the
transmission of new packets.
The source will instead receive a single cumulative ACK that reopens
the entire advertised window, but as explained above, the source then
uses slow start to restart the flow of data rather than dumping a whole
window’s worth of data on the network all at once.
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Slow Start
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Although the source is using slow start again, it now
knows more information than it did at the beginning of
a connection.
Specifically, the source has a current (and useful)
value of CongestionWindow; this is the value of
CongestionWindow that existed prior to the last
packet loss, divided by 2 as a result of the loss.
We can think of this as the “target” congestion
window.
Slow start is used to rapidly increase the sending rate
up to this value, and then additive increase is used
beyond this point.
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Slow Start
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Notice that we have a small bookkeeping problem to
take care of, in that we want to remember the “target”
congestion window resulting from multiplicative
decrease as well as the “actual” congestion window
being used by slow start.
To address this problem, TCP introduces a temporary
variable to store the target window, typically called
CongestionThreshold, that is set equal to the
CongestionWindow value that results from
multiplicative decrease.
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Slow Start
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The variable CongestionWindow is then reset to one
packet, and it is incremented by one packet for every
ACK that is received until it reaches.
CongestionThreshold, at which point it is incremented
by one packet per RTT.
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Slow Start
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In other words, TCP increases the congestion window
as defined by the following code fragment:
{
u_int cw = state->CongestionWindow;
u_int incr = state->maxseg;
if (cw > state->CongestionThreshold)
incr = incr * incr / cw;
state->CongestionWindow = MIN(cw + incr,
TCP_MAXWIN);
}
 where state represents the state of a particular TCP
connection and TCP MAXWIN defines an upper bound on
how large the congestion window is allowed to grow.
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Slow Start
Behavior of TCP congestion control. Colored line = value of
CongestionWindow over time; solid bullets at top of graph =
timeouts; hash marks at top of graph = time when each packet is
transmitted; vertical bars = time when a packet that was
eventually retransmitted was first transmitted.
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Fast Retransmit and Fast Recovery
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The mechanisms described so far were part of the
original proposal to add congestion control to TCP.
It was soon discovered, however, that the coarsegrained implementation of TCP timeouts led to long
periods of time during which the connection went
dead while waiting for a timer to expire.
Because of this, a new mechanism called fast
retransmit was added to TCP.
Fast retransmit is a heuristic that sometimes triggers
the retransmission of a dropped packet sooner than
the regular timeout mechanism.
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Fast Retransmit and Fast Recovery
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The idea of fast retransmit is straightforward. Every
time a data packet arrives at the receiving side, the
receiver responds with an acknowledgment, even if
this sequence number has already been
acknowledged.
Thus, when a packet arrives out of order— that is,
TCP cannot yet acknowledge the data the packet
contains because earlier data has not yet arrived—
TCP resends the same acknowledgment it sent the
last time.
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Fast Retransmit and Fast Recovery
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This second transmission of the same
acknowledgment is called a duplicate ACK.
When the sending side sees a duplicate ACK, it
knows that the other side must have received a
packet out of order, which suggests that an earlier
packet might have been lost.
Since it is also possible that the earlier packet has
only been delayed rather than lost, the sender waits
until it sees some number of duplicate ACKs and then
retransmits the missing packet. In practice, TCP waits
until it has seen three duplicate ACKs before
retransmitting the packet.
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Fast Retransmit and Fast Recovery
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When the fast retransmit mechanism signals
congestion, rather than drop the congestion window
all the way back to one packet and run slow start, it is
possible to use the ACKs that are still in the pipe to
clock the sending of packets.
This mechanism, which is called fast recovery,
effectively removes the slow start phase that happens
between when fast retransmit detects a lost packet
and additive increase begins.
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TCP Congestion Control
Fast Retransmit and Fast Recovery
Trace of TCP with fast retransmit. Colored line = CongestionWindow;
solid bullet = timeout; hash marks = time when each packet is transmitted;
vertical bars = time when a packet that was eventually retransmitted was
first transmitted.
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



It is important to understand that TCP’s strategy is to
control congestion once it happens, as opposed to trying
to avoid congestion in the first place.
In fact, TCP repeatedly increases the load it imposes on
the network in an effort to find the point at which
congestion occurs, and then it backs off from this point.
An appealing alternative, but one that has not yet been
widely adopted, is to predict when congestion is about to
happen and then to reduce the rate at which hosts send
data just before packets start being discarded.
We call such a strategy congestion avoidance, to
distinguish it from congestion control.
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
DEC Bit



The first mechanismwas developed for use on the
Digital Network Architecture (DNA), a connectionless
network with a connection-oriented transport protocol.
This mechanism could, therefore, also be applied to
TCP and IP.
As noted above, the idea here is to more evenly split
the responsibility for congestion control between the
routers and the end nodes.
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DEC Bit




Each router monitors the load it is experiencing and
explicitly notifies the end nodes when congestion is
about to occur.
This notification is implemented by setting a binary
congestion bit in the packets that flow through the
router; hence the name DECbit.
The destination host then copies this congestion bit
into the ACK it sends back to the source.
Finally, the source adjusts its sending rate so as to
avoid congestion
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
DEC Bit


A single congestion bit is added to the packet header.
A router sets this bit in a packet if its average queue
length is greater than or equal to 1 at the time the
packet arrives.
This average queue length is measured over a time
interval that spans the last busy+idle cycle, plus the
current busy cycle.
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
DEC Bit



Essentially, the router calculates the area under the
curve and divides this value by the time interval to
compute the average queue length.
Using a queue length of 1 as the trigger for setting the
congestion bit is a trade-off between significant
queuing (and hence higher throughput) and increased
idle time (and hence lower delay).
In other words, a queue length of 1 seems to optimize
the power function.
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
DEC Bit


The source records how many of its packets resulted
in some router setting the congestion bit.
In particular, the source maintains a congestion
window, just as in TCP, and watches to see what
fraction of the last window’s worth of packets resulted
in the bit being set.
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
DEC Bit


If less than 50% of the packets had the bit set, then
the source increases its congestion window by one
packet.
If 50% or more of the last window’s worth of packets
had the congestion bit set, then the source decreases
its congestion window to 0.875 times the previous
value.
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DEC Bit

The value 50% was chosen as the threshold based on
analysis that showed it to correspond to the peak of
the power curve. The “increase by 1, decrease by
0.875” rule was selected because additive
increase/multiplicative decrease makes the
mechanism stable.
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DEC Bit
Computing average queue length at a
router
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Random Early Detection (RED)

A second mechanism, called random early detection
(RED), is similar to the DECbit scheme in that each
router is programmed to monitor its own queue length,
and when it detects that congestion is imminent, to
notify the source to adjust its congestion window.
RED, invented by Sally Floyd and Van Jacobson in
the early 1990s, differs from the DECbit scheme in
two major ways:
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
Random Early Detection (RED)



The first is that rather than explicitly sending a
congestion notification message to the source, RED is
most commonly implemented such that it implicitly
notifies the source of congestion by dropping one of
its packets.
The source is, therefore, effectively notified by the
subsequent timeout or duplicate ACK.
RED is designed to be used in conjunction with TCP,
which currently detects congestion by means of
timeouts (or some other means of detecting packet
loss such as duplicate ACKs).
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
Random Early Detection (RED)


As the “early” part of the RED acronym suggests, the
gateway drops the packet earlier than it would have
to, so as to notify the source that it should decrease
its congestion window sooner than it would normally
have.
In other words, the router drops a few packets before
it has exhausted its buffer space completely, so as to
cause the source to slow down, with the hope that this
will mean it does not have to drop lots of packets later
on.
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Random Early Detection (RED)



The second difference between RED and DECbit is in
the details of how RED decides when to drop a packet
and what packet it decides to drop.
To understand the basic idea, consider a simple FIFO
queue. Rather than wait for the queue to become
completely full and then be forced to drop each
arriving packet, we could decide to drop each arriving
packet with some drop probability whenever the
queue length exceeds some drop level.
This idea is called early random drop. The RED
algorithm defines the details of how to monitor the
queue length and when to drop a packet.
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
Random Early Detection (RED)

First, RED computes an average queue length using a
weighted running average similar to the one used in
the original TCP timeout computation. That is, AvgLen
is computed as




AvgLen = (1 − Weight) × AvgLen + Weight × SampleLen
where 0 < Weight < 1 and SampleLen is the length of the
queue when a sample measurement is made.
In most software implementations, the queue length is
measured every time a new packet arrives at the
gateway.
In hardware, it might be calculated at some fixed
sampling interval.
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Random Early Detection (RED)


Second, RED has two queue length thresholds that trigger
certain activity: MinThreshold and MaxThreshold.
When a packet arrives at the gateway, RED compares the
current AvgLen with these two thresholds, according to the
following rules:

if AvgLen  MinThreshold


if MinThreshold < AvgLen < MaxThreshold



 queue the packet
 calculate probability P
 drop the arriving packet with probability P
if MaxThreshold  AvgLen

 drop the arriving packet
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
Random Early Detection (RED)
 P is a function of both AvgLen and how long it has
been since the last packet was dropped.
 Specifically, it is computed as follows:


TempP = MaxP × (AvgLen − MinThreshold)/(MaxThreshold − MinThreshold)
P = TempP/(1 − count × TempP)
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Random Early Detection (RED)
RED thresholds on a FIFO queue
78
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Random Early Detection (RED)
Drop probability function for RED
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
Source-based Congestion Avoidance


The general idea of these techniques is to watch for
some sign from the network that some router’s queue
is building up and that congestion will happen soon if
nothing is done about it.
For example, the source might notice that as packet
queues build up in the network’s routers, there is a
measurable increase in the RTT for each successive
packet it sends.
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
Source-based Congestion Avoidance

One particular algorithm exploits this observation as
follows:


The congestion window normally increases as in TCP, but
every two round-trip delays the algorithm checks to see if the
current RTT is greater than the average of the minimum and
maximum RTTs seen so far.
If it is, then the algorithm decreases the congestion window
by one-eighth.
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Source-based Congestion Avoidance


A second algorithm does something similar. The
decision as to whether or not to change the current
window size is based on changes to both the RTT and
the window size.
The window is adjusted once every two round-trip
delays based on the product




(CurrentWindow − OldWindow)×(CurrentRTT − OldRTT)
If the result is positive, the source decreases the window size by
one-eighth;
if the result is negative or 0, the source increases the window by one
maximum packet size.
Note that the window changes during every adjustment; that is, it
oscillates around its optimal point.
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Chapter 6
Quality of Service
For many years, packet-switched networks have offered
the promise of supporting multimedia applications, that
is, those that combine audio, video, and data.
After all, once digitized, audio and video information
become like any other form of data—a stream of bits to
be transmitted. One obstacle to the fulfillment of this
promise has been the need for higher-bandwidth links.
Recently, however, improvements in coding have
reduced the bandwidth needs of audio and video
applications, while at the same time link speeds have
increased.
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Quality of Service
There is more to transmitting audio and video over a
network than just providing sufficient bandwidth,
however.
Participants in a telephone conversation, for example,
expect to be able to converse in such a way that one
person can respond to something said by the other and
be heard almost immediately.
Thus, the timeliness of delivery can be very important.
We refer to applications that are sensitive to the
timeliness of data as real-time applications.
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Voice and video applications tend to be the canonical
examples, but there are others such as industrial
control—you would like a command sent to a robot arm
to reach it before the arm crashes into something.
Even file transfer applications can have timeliness
constraints, such as a requirement that a database
update complete overnight before the business that
needs the data resumes on the next day.
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



The distinguishing characteristic of real-time applications
is that they need some sort of assurance from the
network that data is likely to arrive on time (for some
definition of “on time”).
Whereas a non-real-time application can use an end-toend retransmission strategy to make sure that data
arrives correctly, such a strategy cannot provide
timeliness.
This implies that the network will treat some packets
differently from others—something that is not done in the
best-effort model.
A network that can provide these different levels of
service is often said to support quality of service (QoS).
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Real-Time Applications



Data is generated by collecting samples from a
microphone and digitizing them using an A D
converter
The digital samples are placed in packets which are
transmitted across the network and received at the
other end
At the receiving host the data must be played back at
some appropriate rate
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Real-Time Applications





For example, if voice samples were collected at a rate
of one per 125 s, they should be played back at the
same rate
We can think of each sample as having a particular
playback time
The point in time at which it is needed at the receiving
host
In this example, each sample has a playback time that
is 125 s later than the preceding sample
If data arrives after its appropriate playback time, it is
useless
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Real-Time Applications




For some audio applications, there are limits to how
far we can delay playing back data
It is hard to carry on a conversation if the time
between when you speak and when your listener
hears you is more than 300 ms
We want from the network a guarantee that all our
data will arrive within 300 ms
If data arrives early, we buffer it until playback time
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Real-Time Applications
A playback buffer
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Taxonomy of Real-Time Applications


The first characteristic by which we can categorize
applications is their tolerance of loss of data, where
“loss” might occur because a packet arrived too late to
be played back as well as arising from the usual
causes in the network.
On the one hand, one lost audio sample can be
interpolated from the surrounding samples with
relatively little effect on the perceived audio quality. It
is only as more and more samples are lost that quality
declines to the point that the speech becomes
incomprehensible.
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
Taxonomy of Real-Time Applications


On the other hand, a robot control program is likely to
be an example of a real-time application that cannot
tolerate loss—losing the packet that contains the
command instructing the robot arm to stop is
unacceptable.
Thus, we can categorize real-time applications as
tolerant or intolerant depending on whether they can
tolerate occasional loss
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
Taxonomy of Real-Time Applications

A second way to characterize real-time applications is
by their adaptability.

For example, an audio application might be able to adapt to
the amount of delay that packets experience as they traverse
the network.



If we notice that packets are almost always arriving within 300 ms of
being sent, then we can set our playback point accordingly, buffering
any packets that arrive in less than 300 ms.
Suppose that we subsequently observe that all packets are arriving
within 100 ms of being sent.
If we moved up our playback point to 100 ms, then the users of the
application would probably perceive an improvement. The process of
shifting the playback point would actually require us to play out samples
at an increased rate for some period of time.
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
Taxonomy of Real-Time Applications



We call applications that can adjust their playback
point delay-adaptive applications.
Another class of adaptive applications are rate
adaptive. For example, many video coding algorithms
can trade off bit rate versus quality. Thus, if we find
that the network can support a certain bandwidth, we
can set our coding parameters accordingly.
If more bandwidth becomes available later, we can
change parameters to increase the quality.
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Taxonomy of Real-Time Applications
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Approaches to QoS Support




fine-grained approaches, which provide QoS to
individual applications or flows
coarse-grained approaches, which provide QoS to
large classes of data or aggregated traffic
In the first category we find “Integrated Services,” a
QoS architecture developed in the IETF and often
associated with RSVP (Resource Reservation
Protocol).
In the second category lies “Differentiated Services,”
which is probably the most widely deployed QoS
mechanism.
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Integrated Services (RSVP)



The term “Integrated Services” (often called IntServ
for short) refers to a body of work that was produced
by the IETF around 1995–97.
The IntServ working group developed specifications of
a number of service classes designed to meet the
needs of some of the application types described
above.
It also defined how RSVP could be used to make
reservations using these service classes.
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
Integrated Services (RSVP)

Service Classes


Guaranteed Service
 The network should guarantee that the maximum delay
that any packet will experience has some specified value
Controlled Load Service
 The aim of the controlled load service is to emulate a
lightly loaded network for those applications that request
the service, even though the network as a whole may in
fact be heavily loaded
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
Integrated Services (RSVP)

Overview of Mechanisms

Flowspec



Admission Control


With a best-effort service we can just tell the network where we want
our packets to go and leave it at that, a real-time service involves telling
the network something more about the type of service we require
The set of information that we provide to the network is referred to as a
flowspec.
When we ask the network to provide us with a particular service, the
network needs to decide if it can in fact provide that service. The
process of deciding when to say no is called admission control.
Resource Reservation

We need a mechanism by which the users of the network and the
components of the network itself exchange information such as
requests for service, flowspecs, and admission control decisions. We
refer to this process as resource reservation
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
Integrated Services (RSVP)

Overview of Mechanisms

Packet Scheduling



Finally, when flows and their requirements have been described, and
admission control decisions have been made, the network switches and
routers need to meet the requirements of the flows.
A key part of meeting these requirements is managing the way packets
are queued and scheduled for transmission in the switches and routers.
This last mechanism is packet scheduling.
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Integrated Services (RSVP)

Flowspec

There are two separable parts to the flowspec:





The part that describes the flow’s traffic characteristics (called the
TSpec) and
The part that describes the service requested from the network (the
RSpec).
The RSpec is very service specific and relatively easy to describe.
For example, with a controlled load service, the RSpec is trivial: The
application just requests controlled load service with no additional
parameters.
With a guaranteed service, you could specify a delay target or bound.
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
Integrated Services (RSVP)

Flowspec

Tspec


We need to give the network enough information about the
bandwidth used by the flow to allow intelligent admission control
decisions to be made
For most applications, the bandwidth is not a single number


It varies constantly
A video application will generate more bits per second when the
scene is changing rapidly than when it is still

Just knowing the long term average bandwidth is not enough
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
Integrated Services (RSVP)

Flowspec


Suppose 10 flows arrive at a switch on separate ports and
they all leave on the same 10 Mbps link
If each flow is expected to send no more than 1 Mbps


If these are variable bit applications such as compressed
video




No problem
They will occasionally send more than the average rate
If enough sources send more than average rates, then the
total rate at which data arrives at the switch will be more than
10 Mbps
This excess data will be queued
The longer the condition persists, the longer the queue will
get
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Integrated Services (RSVP)

Flowspec







One way to describe the Bandwidth characteristics of sources
is called a Token Bucket Filter
The filter is described by two parameters
 A token rate r
 A bucket depth B
To be able to send a byte, a token is needed
To send a packet of length n, n tokens are needed
Initially there are no tokens
Tokens are accumulated at a rate of r per second
No more than B tokens can be accumulated
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
Integrated Services (RSVP)

Flowspec


We can send a burst of as many as B bytes into the network
as fast as we want, but over significant long interval we
cannot send more than r bytes per second
This information is important for admission control algorithm
when it tries to find out whether it can accommodate new
request for service
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



Chapter 6
Quality of Service
Flowspec
The figure illustrates how a token
bucket can be used to characterize a
flow’s Bandwidth requirement
For simplicity, we assume each flow
can send data as individual bytes
rather than as packets
Flow A generates data at a steady
rate of 1 MBps


So it can be described by a token
bucket filter with a rate r = 1 MBps
and a bucket depth of 1 byte
This means that it receives tokens at
a rate of 1 MBps but it cannot store
more than 1 token, it spends them
immediately
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


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Quality of Service
Flowspec
Flow B sends at a rate that averages
out to 1 MBps over the long term,
but does so by sending at 0.5 MBps
for 2 seconds and then at 2 MBps
for 1 second
Since the token bucket rate r is a
long term average rate, flow B can
be described by a token bucket with
a rate of 1 MBps
Unlike flow A, however flow B needs
a bucket depth B of at least 1 MB, so
that it can store up tokens while it
sends at less than 1 MBps to be
used when it sends at 2 MBps
107

Flowspec

For the first 2 seconds, it receives
tokens at a rate of 1 MBps but
spends them at only 0.5 MBps,

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Quality of Service
So it can save up 2  0.5 = 1 MB of
tokens which it spends at the 3rd
second
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Integrated Services (RSVP)

Admission Control

The idea behind admission control is simple: When some
new flow wants to receive a particular level of service,
admission control looks at the TSpec and RSpec of the flow
and tries to decide if the desired service can be provided to
that amount of traffic, given the currently available resources,
without causing any previously admitted flow to receive worse
service than it had requested. If it can provide the service, the
flow is admitted; if not, then it is denied.
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
Integrated Services (RSVP)

Reservation Protocol



While connection-oriented networks have always needed
some sort of setup protocol to establish the necessary virtual
circuit state in the switches, connectionless networks like the
Internet have had no such protocols.
However we need to provide a lot more information to our
network when we want a real-time service from it.
While there have been a number of setup protocols proposed
for the Internet, the one on which most current attention is
focused is called Resource Reservation Protocol (RSVP).
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Integrated Services (RSVP)

Reservation Protocol



One of the key assumptions underlying RSVP is that it should
not detract from the robustness that we find in today’s
connectionless networks.
Because connectionless networks rely on little or no state
being stored in the network itself, it is possible for routers to
crash and reboot and for links to go up and down while endto-end connectivity is still maintained.
RSVP tries to maintain this robustness by using the idea of
soft state in the routers.
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Integrated Services (RSVP)

Reservation Protocol



Another important characteristic of RSVP is that it aims to
support multicast flows just as effectively as unicast flows
Initially, consider the case of one sender and one receiver
trying to get a reservation for traffic flowing between them.
There are two things that need to happen before a receiver
can make the reservation.
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
Integrated Services (RSVP)

Reservation Protocol



First, the receiver needs to know what traffic the sender is
likely to send so that it can make an appropriate reservation.
That is, it needs to know the sender’s TSpec.
Second, it needs to know what path the packets will follow
from sender to receiver, so that it can establish a resource
reservation at each router on the path. Both of these
requirements can be met by sending a message from the
sender to the receiver that contains the TSpec.
Obviously, this gets the TSpec to the receiver. The other
thing that happens is that each router looks at this message
(called a PATH message) as it goes past, and it figures out
the reverse path that will be used to send reservations from
the receiver back to the sender in an effort to get the
reservation to each router on the path.
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
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Quality of Service
Reservation Protocol





Having received a PATH message, the receiver sends a
reservation back “up” the multicast tree in a RESV message.
This message contains the sender’s TSpec and an RSpec
describing the requirements of this receiver.
Each router on the path looks at the reservation request and
tries to allocate the necessary resources to satisfy it. If the
reservation can be made, the RESV request is passed on to
the next router.
If not, an error message is returned to the receiver who made
the request. If all goes well, the correct reservation is installed
at every router between the sender and the receiver.
As long as the receiver wants to retain the reservation, it
sends the same RESV message about once every 30
seconds.
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Reservation Protocol
Making reservations on a multicast tree
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
Integrated Services (RSVP)

Packet Classifying and Scheduling
 Once we have described our traffic and our desired
network service and have installed a suitable
reservation at all the routers on the path, the only
thing that remains is for the routers to actually
deliver the requested service to the data packets.
There are two things that need to be done:


Associate each packet with the appropriate reservation
so that it can be handled correctly, a process known as
classifying packets.
Manage the packets in the queues so that they receive
the service that has been requested, a process known as
packet scheduling.
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Differentiated Services
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Whereas the Integrated Services architecture
allocates resources to individual flows, the
Differentiated Services model (often called DiffServ for
short) allocates resources to a small number of
classes of traffic.
In fact, some proposed approaches to DiffServ simply
divide traffic into two classes.
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
Differentiated Services



Suppose that we have decided to enhance the besteffort service model by adding just one new class,
which we’ll call “premium.”
Clearly we will need some way to figure out which
packets are premium and which are regular old best
effort.
Rather than using a protocol like RSVP to tell all the
routers that some flow is sending premium packets, it
would be much easier if the packets could just identify
themselves to the router when they arrive. This could
obviously be done by using a bit in the packet
header—if that bit is a 1, the packet is a premium
packet; if it’s a 0, the packet is best effort
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With this in mind, there are two questions we need to
address:


Who sets the premium bit, and under what circumstances?
What does a router do differently when it sees a packet with
the bit set?
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There are many possible answers to the first question,
but a common approach is to set the bit at an
administrative boundary.
For example, the router at the edge of an Internet
service provider’s network might set the bit for packets
arriving on an interface that connects to a particular
company’s network.
The Internet service provider might do this because
that company has paid for a higher level of service
than best effort.
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Assuming that packets have been marked in some
way, what do the routers that encounter marked
packets do with them?
Here again there are many answers. In fact, the IETF
standardized a set of router behaviors to be applied to
marked packets. These are called “per-hop behaviors”
(PHBs), a term that indicates that they define the
behavior of individual routers rather than end-to-end
services
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The Expedited Forwarding (EF) PHB


One of the simplest PHBs to explain is known as “expedited
forwarding” (EF). Packets marked for EF treatment should be
forwarded by the router with minimal delay and loss.
The only way that a router can guarantee this to all EF
packets is if the arrival rate of EF packets at the router is
strictly limited to be less than the rate at which the router can
forward EF packets.
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The Assured Forwarding (AF) PHB
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
The “assured forwarding” (AF) PHB has its roots in an
approach known as “RED with In and Out” (RIO) or
“Weighted RED,” both of which are enhancements to the
basic RED algorithm.
For our two classes of traffic, we have two separate drop
probability curves. RIO calls the two classes “in” and “out” for
reasons that will become clear shortly.
Because the “out” curve has a lower MinThreshold than the
“in” curve, it is clear that, under low levels of congestion, only
packets marked “out” will be discarded by the RED algorithm.
If the congestion becomes more serious, a higher percentage
of “out” packets are dropped, and then if the average queue
length exceeds Minin, RED starts to drop “in” packets as well.
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The Assured Forwarding (AF) PHB
RED with In and Out drop probabilities
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We have discussed different resource allocation
mechanisms
We have discussed different queuing
mechanisms
We have discussed TCP Congestion Control
mechanisms
We have discussed Congestion Avoidance
mechanisms
We have discussed Quality of Services
Chapter 6
Summary
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